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<p>The task of network security is to keep services available at all times by dealing with hacker attacks. One of the mechanisms obtainable is the Intrusion Detection System (IDS) which is used to sense and classify any abnormal actions. Therefore, the IDS system should always be up-to-date with the latest hacker attack signatures to keep services confidential, safe, and available. IDS speed is a very important issue in addition to learning new attacks. A modified selection strategy based on features was proposed in this paper one of the important swarm intelligent algorithms is the Meerkat Clan Algorithm (MCA). Meerkat Clan Algorithm has good diversity solutions through its neighboring generation conduct and it was used to solve several problems. The proposed strategy benefitted from mutual information to increase the performance and decrease the consumed time. Two datasets (NSL-KDD &amp; UNSW-NB15) for Network Intrusion Detection Systems (NIDS) have been used to verify the performance of the proposed algorithm. The experimental findings indicate that, compared to other approaches, the proposed algorithm produces good results in a minimum of time.</p><p><strong> </strong></p>

This paper presents a proposed approach for assessing video quality without reference and makes use of a data hiding technique to embed a fragile mark into video frame by using Discreet Cosine Transform (DCT) and quantization. The fragile mark is random watermark generator using stream cipher based on Linear Feedback Shift Register (LFSR) and using Geffe generator to give a balanced distribution of zeros and ones in its output. The frame format consists of three color components, Red (R), Green (G) and Blue (B) for individual pixel, the watermark data should be hidden in Red (R) color channel to ensure the best recovery of the watermark. At the receiver, the mark is extracted from decoded video without any original reference video sequences. After extracted watermark, quality measure of the video is obtained by computing the degradation of the extracted mark. The results of this experiment indicate identical values of the Normalized Cross-correlation (NC) for three categories of real quality that have been proposed (good, low, bad) with the perceived quality. The experiment shows the error of the proposed system is 7% while correct ratio is 93% , the salt and pepper noise gives good results without errors while the Dust& Scratches noise gives highest error in the system. Results shown that the proposed algorithm provides a good estimation for the video quality when adding noise.

In recent years, social media has been increasing widely and obviously as a media for users expressing their emotions and feelings through thousands of posts and comments related to tourism companies. As a consequence, it became difficult for tourists to read all the comments to determine whether these opinions are positive or negative to assess the success of a tourism company. In this paper, a modest model is proposed to assess e-tourism companies using Iraqi dialect reviews collected from Facebook. The reviews are analyzed using text mining techniques for sentiment classification. The generated sentiment words are classified into positive, negative and neutral comments by utilizing Rough Set Theory, Naïve Bayes and K-Nearest Neighbor methods. After experimental results, it was determined that out of 71 tested Iraqi tourism companies, 28% from these companies have very good assessment, 26% from these companies have good assessment, 31% from these companies have medium assessment, 4% from these companies have acceptance assessment and 11% from these companies have bad assessment. These results helped the companies to improve their work and programs responding sufficiently and quickly to customer demands.